// Project brief
Real-time persuasion training game — talk your way past an AI guard, investor, or friend, and every sentence is instantly scored and reacted to.
Projecthackathon
Built at JEVATHON (w/ The AI Collective)
Sreak
Real-time persuasion training game — talk your way past an AI guard, investor, or friend, and every sentence is instantly scored and reacted to.
// latest_project_recording.mp4
// More from this event
Keep exploring what builders shipped.
Worth Your Saturday
Worth Your Saturday
Worth Your Saturday is a decision-intelligence prototype that helps people discover what they actually won’t compromise on before spending their time. Using apartment hunting as the demo, Jev speculatively evaluates 1,872 evidence-level distinctions across five apartments; as a conversation clarifies the user’s priorities, the same evidence fields are re-projected with zero additional Jev evaluations to surface meaningful matches, tradeoffs, and unresolved questions. The result isn’t another ranked list—it’s a realistic Saturday of tours worth taking, delivered through an agent connected to iMessage with Photon Spectrum. The broader primitive is intent-conditioned salience: evaluate evidence broadly, discover what matters to the person, preserve consequential uncertainty, and allocate scarce human attention accordingly. Https://worth-your-saturday.vercel.app
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HackerSquad project
UnSlop
Slop isn't just AI, it can also be human. Unslop will input a website and cut out all the ineffective fluff/slop with context decisions and browserbase lookup centered around jev decision making.
TypeSafe AIBrowserbase
KYC-Sentinel
KYC Sentinel
KYC Sentinel: adverse-media screening for AML/KYC analysts. Jev judges, code decides, the LLM only writes. Compliance analysts waste hours clearing irrelevant news hits: same-name strangers, victims, passing mentions. KYC Sentinel pulls news for every client in a portfolio. Jev (TypeSafe AI) judges each client–article pair: same person? the perpetrator? what risk? how severe? A pure, unit-tested router then sorts every client into CLEAR / REVIEW / FLAGGED / NO_COVERAGE. • Fails closed: any uncertainty or model error means REVIEW, never CLEAR. • Autonomy Dial: a compliance manager drags strictness and the whole portfolio re-routes instantly from stored Jev probabilities, with zero new model calls. • Thresholds live in code, not prompts. The LLM only writes analyst memos, on demand, for flagged/review clients, citing sources. It never decides. • Full audit log of every decision, plus a live receipts meter. Results on a 26-client portfolio: 37 Jev calls, 0 errors, 0.9s, $0.0011, about 145× cheaper than the same job on a frontier LLM (est. $0.16). Famous-name collisions (Michael Jordan, Michael Cohen) are cleared as "different person" even when the other person's news is damning. Victims and identity-theft targets are cleared as "not the subject". Vague common-name mentions go to REVIEW. Documented fraud cases and a Tulsa accountant who shares Taylor Swift's name are surfaced, while the pop star's news is ignored. Stack: Jev (TypeSafe AI), FastAPI, GDELT, Gemini for memos, vanilla JS. 77 tests. Data note: GDELT rate-limited our network, so several clients use clearly labelled synthetic articles (domain synthetic-demo.local). Everything downstream runs identically.
TypeSafe AI